pytorch · cpuhrsch · Dec 11, 2019 · Dec 4, 2019 · Dec 4, 2019 · Dec 4, 2019
diff --git a/.circleci/config.yml b/.circleci/config.yml
@@ -6,52 +6,24 @@ version: 2
 jobs:
   build:
     docker:
-      # specify the version you desire here
-      # use `-browsers` prefix for selenium tests, e.g. `3.6.1-browsers`
-      - image: circleci/python:3.6.1
-
-      # Specify service dependencies here if necessary
-      # CircleCI maintains a library of pre-built images
-      # documented at https://circleci.com/docs/2.0/circleci-images/
-      # - image: circleci/postgres:9.4
+      - image: continuumio/miniconda3
 
     working_directory: ~/repo
 
     steps:
       - checkout
 
-      # Download and cache dependencies
-      - restore_cache:
-          keys:
-            - v1-dependencies-{{ checksum "requirements.txt" }}
-            # fallback to using the latest cache if no exact match is found
-            - v1-dependencies-
-
-      - run:
-          name: install dependencies
-          command: |
-            python3 -m venv venv
-            . venv/bin/activate
-            pip3 install -q -r requirements.txt
-            pip3 install -q torch
-
-      - save_cache:
-          paths:
-            - ./venv
-          key: v1-dependencies-{{ checksum "requirements.txt" }}
-
       - run:
           name: run tests
           command: |
-            . venv/bin/activate
-            pip3 install .
+            conda install pytorch cpuonly -c pytorch-nightly -q -y
+            apt-get update
+            apt-get install -y gcc
+            apt-get install -y g++
+            pip install .
             python test/test_nested_tensor_autograd.py
             python test/test_nested_tensor_class.py
             python test/test_nested_tensor_functional.py
             python test/test_nested_tensor_masking.py
             python test/test_nested_tensor_nary.py
             python test/test_nested_tensor_tensorwise.py
-
-      - store_artifacts:
-          path: test-reports
-          destination: test-reports
diff --git a/README.md b/README.md
@@ -1,6 +1,7 @@
 # The nestedtensor package
 
-NOTE: PLEASE NOTE, NESTEDTENSOR IS UNDER ACTIVE DEVELOPMENT AND EVERYTHING HERE IS SUBJECT TO CHANGE.
+NOTE: nestedtensor is under active development and various aspects may change.
+NOTE: We test and develop against nightlies! Please use the most recent version of PyTorch if you plan to use this code.
 
 ## Motivation
 
@@ -44,7 +45,7 @@ Import nested tensors and torch via ```from nestedtensor import torch```
 ### Creation
 
 ```
-nt = torch.nested_tensor(
+nt = nestedtensor.nested_tensor(
     [
         [
             torch.rand(2, 3),
@@ -62,13 +63,15 @@ b = torch.tensor([[2, 2],
                   [3, 3],
                   [4, 4],
                   [5, 5]])
-nt2 = torch.nested_tensor([[a],[b]])
+nt2 = nestedtensor.nested_tensor([[a],[b]])
 ```
 
 The level of nesting is inferred from the input. The constructor always copies. Whatever you pass into the constructor will share no data with what the constructor returns. This matches torch.tensor's behavior.
 
 If given a NestedTensor or Tensor it will return a detached copy, which is consistent with the behavior of torch.tensor. Remember that you cannot mix Tensors and NestedTensors within a given list.
 
+A side-note on naming: nestedtensor is a python packed and as such [shouldn't have underscores and is lower case](https://www.python.org/dev/peps/pep-0008/#package-and-module-names), but nested_tensor is a python function and as [such should use underscores](https://www.python.org/dev/peps/pep-0008/#function-and-variable-names) in contrast to the [CapWorded NestedTensor class](https://www.python.org/dev/peps/pep-0008/#class-names).
+
 ### Conversion/unbind()
 A user can retrieve the constituent Tensors via unbind. Unbind is currently used by torch to turn Tensors into tuples of Tensors. Unbind always returns a tuple of views.
 
@@ -80,7 +83,7 @@ A user can retrieve the constituent Tensors via unbind. Unbind is currently used
 ...        [torch.rand(3, 2)]
 ...     ]
 >>>
->>> b = torch.nested_tensor(a)
+>>> b = nestedtensor.nested_tensor(a)
 >>> print(b)
 nested_tensor([
     [
@@ -136,8 +139,8 @@ tensor([ 9, 11])
 >>> print(simple_fn(c, d))
 tensor([10, 10])
 >>>
->>> n = torch.nested_tensor([a, c])
->>> m = torch.nested_tensor([b, d])
+>>> n = nestedtensor.nested_tensor([a, c])
+>>> m = nestedtensor.nested_tensor([b, d])
 >>> print(simple_fn(n, m))
 nested_tensor([
     tensor([ 9, 11]),

diff --git a/examples/basic.ipynb b/examples/basic.ipynb
@@ -15,7 +15,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nestedtensor import torch\n",
+    "import torch\n",
+    "import nestedtensor\n",
     "from IPython.display import Markdown, display\n",
     "\n",
     "def print_eval(s):\n",
@@ -54,23 +55,23 @@
      "text": [
       "nested_tensor([\n",
       "\t[\n",
-      "\t\ttensor([[0.8264, 0.2200, 0.4197],\n",
-      "\t\t        [0.6789, 0.7460, 0.1694]]),\n",
-      "\t\ttensor([[0.7467, 0.8433, 0.6429, 0.9890, 0.0170],\n",
-      "\t\t        [0.6297, 0.3899, 0.7025, 0.0812, 0.9585],\n",
-      "\t\t        [0.1113, 0.4260, 0.4245, 0.7971, 0.7910],\n",
-      "\t\t        [0.7077, 0.6765, 0.0228, 0.5461, 0.4095]])\n",
+      "\t\ttensor([[0.1525, 0.9457, 0.8438],\n",
+      "\t\t        [0.6784, 0.9376, 0.5344]]),\n",
+      "\t\ttensor([[0.5654, 0.6054, 0.2726, 0.8868, 0.3417],\n",
+      "\t\t        [0.1225, 0.4104, 0.9022, 0.6978, 0.2081],\n",
+      "\t\t        [0.5641, 0.2983, 0.7589, 0.5495, 0.1304],\n",
+      "\t\t        [0.1999, 0.3803, 0.0336, 0.4855, 0.9838]])\n",
       "\t],\n",
       "\t[\n",
-      "\t\ttensor([[0.0660, 0.9756]])\n",
+      "\t\ttensor([[0.8105, 0.6778]])\n",
       "\t]\n",
       "])\n",
       "\n"
      ]
     }
    ],
    "source": [
-    "nt = torch.nested_tensor(\n",
+    "nt = nestedtensor.nested_tensor(\n",
     "    [\n",
     "        [\n",
     "            torch.rand(2, 3),\n",
@@ -227,7 +228,7 @@
     "                  [3, 3],\n",
     "                  [4, 4],\n",
     "                  [5, 5]])\n",
-    "nt2 = torch.nested_tensor([[a],[b]])\n",
+    "nt2 = nestedtensor.nested_tensor([[a],[b]])\n",
     "print_eval(\"nt2.nested_dim()\")\n",
     "print_eval(\"nt2.tensor_dim()\")\n",
     "print_eval(\"nt2.dim()\")"
@@ -281,15 +282,15 @@
      "text": [
       "nested_tensor([\n",
       "\t[\n",
-      "\t\ttensor([[0.8264, 0.2200, 0.4197],\n",
-      "\t\t        [0.6789, 0.7460, 0.1694]]),\n",
-      "\t\ttensor([[0.7467, 0.8433, 0.6429, 0.9890, 0.0170],\n",
-      "\t\t        [0.6297, 0.3899, 0.7025, 0.0812, 0.9585],\n",
-      "\t\t        [0.1113, 0.4260, 0.4245, 0.7971, 0.7910],\n",
-      "\t\t        [0.7077, 0.6765, 0.0228, 0.5461, 0.4095]])\n",
+      "\t\ttensor([[0.1525, 0.9457, 0.8438],\n",
+      "\t\t        [0.6784, 0.9376, 0.5344]]),\n",
+      "\t\ttensor([[0.5654, 0.6054, 0.2726, 0.8868, 0.3417],\n",
+      "\t\t        [0.1225, 0.4104, 0.9022, 0.6978, 0.2081],\n",
+      "\t\t        [0.5641, 0.2983, 0.7589, 0.5495, 0.1304],\n",
+      "\t\t        [0.1999, 0.3803, 0.0336, 0.4855, 0.9838]])\n",
       "\t],\n",
       "\t[\n",
-      "\t\ttensor([[0.0660, 0.9756]])\n",
+      "\t\ttensor([[0.8105, 0.6778]])\n",
       "\t]\n",
       "])\n",
       "\n"
@@ -621,7 +622,7 @@
     {
      "data": {
       "text/markdown": [
-       "**<span style='color:darkred'>$ torch.nested_tensor_from_tensor_mask(tensor, mask)</span>**"
+       "**<span style='color:darkred'>$ nestedtensor.nested_tensor_from_tensor_mask(tensor, mask)</span>**"
       ],
       "text/plain": [
        "<IPython.core.display.Markdown object>"
@@ -646,7 +647,7 @@
     {
      "data": {
       "text/markdown": [
-       "**<span style='color:darkred'>$ torch.nested_tensor_from_padded_tensor(tensor, padding=0)</span>**"
+       "**<span style='color:darkred'>$ nestedtensor.nested_tensor_from_padded_tensor(tensor, padding=0)</span>**"
       ],
       "text/plain": [
        "<IPython.core.display.Markdown object>"
@@ -688,9 +689,9 @@
     "         [ True,  True,  True,  True]]])\n",
     "print_eval(\"tensor\")\n",
     "print_eval(\"mask\")\n",
-    "nt2 = torch.nested_tensor_from_tensor_mask(tensor, mask)\n",
-    "print_eval(\"torch.nested_tensor_from_tensor_mask(tensor, mask)\")\n",
-    "print_eval(\"torch.nested_tensor_from_padded_tensor(tensor, padding=0)\")"
+    "nt2 = nestedtensor.nested_tensor_from_tensor_mask(tensor, mask)\n",
+    "print_eval(\"nestedtensor.nested_tensor_from_tensor_mask(tensor, mask)\")\n",
+    "print_eval(\"nestedtensor.nested_tensor_from_padded_tensor(tensor, padding=0)\")"
    ]
   },
   {
@@ -795,12 +796,12 @@
      "output_type": "stream",
      "text": [
       "nested_tensor([\n",
-      "\ttensor([[0.8264, 0.2200, 0.4197],\n",
-      "\t        [0.6789, 0.7460, 0.1694]]),\n",
-      "\ttensor([[0.7467, 0.8433, 0.6429, 0.9890, 0.0170],\n",
-      "\t        [0.6297, 0.3899, 0.7025, 0.0812, 0.9585],\n",
-      "\t        [0.1113, 0.4260, 0.4245, 0.7971, 0.7910],\n",
-      "\t        [0.7077, 0.6765, 0.0228, 0.5461, 0.4095]])\n",
+      "\ttensor([[0.1525, 0.9457, 0.8438],\n",
+      "\t        [0.6784, 0.9376, 0.5344]]),\n",
+      "\ttensor([[0.5654, 0.6054, 0.2726, 0.8868, 0.3417],\n",
+      "\t        [0.1225, 0.4104, 0.9022, 0.6978, 0.2081],\n",
+      "\t        [0.5641, 0.2983, 0.7589, 0.5495, 0.1304],\n",
+      "\t        [0.1999, 0.3803, 0.0336, 0.4855, 0.9838]])\n",
       "])\n",
       "\n"
      ]
@@ -822,7 +823,7 @@
      "output_type": "stream",
      "text": [
       "nested_tensor([\n",
-      "\ttensor([[0.0660, 0.9756]])\n",
+      "\ttensor([[0.8105, 0.6778]])\n",
       "])\n",
       "\n"
      ]
@@ -857,15 +858,15 @@
      "text": [
       "nested_tensor([\n",
       "\t[\n",
-      "\t\ttensor([[0.6776, 0.9759, 0.9132],\n",
-      "\t\t        [0.7783, 0.7344, 0.9857]]),\n",
-      "\t\ttensor([[0.7467, 0.8433, 0.6429, 0.9890, 0.0170],\n",
-      "\t\t        [0.6297, 0.3899, 0.7025, 0.0812, 0.9585],\n",
-      "\t\t        [0.1113, 0.4260, 0.4245, 0.7971, 0.7910],\n",
-      "\t\t        [0.7077, 0.6765, 0.0228, 0.5461, 0.4095]])\n",
+      "\t\ttensor([[0.9884, 0.5852, 0.6646],\n",
+      "\t\t        [0.7786, 0.5917, 0.8606]]),\n",
+      "\t\ttensor([[0.5654, 0.6054, 0.2726, 0.8868, 0.3417],\n",
+      "\t\t        [0.1225, 0.4104, 0.9022, 0.6978, 0.2081],\n",
+      "\t\t        [0.5641, 0.2983, 0.7589, 0.5495, 0.1304],\n",
+      "\t\t        [0.1999, 0.3803, 0.0336, 0.4855, 0.9838]])\n",
       "\t],\n",
       "\t[\n",
-      "\t\ttensor([[0.0660, 0.9756]])\n",
+      "\t\ttensor([[0.8105, 0.6778]])\n",
       "\t]\n",
       "])\n",
       "\n"